Extended reality platform and machine learning engine for automated code error resolution

Abstract

Systems, computer program products, and methods are described herein for automated code resolution in an extended reality environment. The present invention allows a user (such as a software developer) to view source code discrepancies in real time using an extended reality (XR) environment. In this regard, the present invention focuses on electronic applications (and the electronic work products/electronic data hosted thereon) and represents a combined view of real-time applications and application requirements within an XR environment. A user may then visualize discrepancies between the current application and the application requirements via an XR platform (accessible using a virtual/augmented/mixed reality device) and proactively make edits, approvals, or otherwise interact with said application. The system may also be configured to automatically alter the source code to resolve said discrepancies.

What is claimed is: 
     
       1. A system for automated code resolution in an extended reality environment, the system comprising:
 at least one non-transitory storage device storing an extended reality platform; and 
 at least one processing device coupled to the at least one non-transitory storage device, wherein the at least one processing device is configured to: 
 electronically receive a first user input comprising a selection of an error; 
 build a first display based on a plurality of application requirements associated with the error; 
 build a second display based on a source code associated with the error; 
 identify, via a first machine learning algorithm, a plurality of first objects associated with the first display and a plurality of second objects associated with the second display; 
 identify a plurality of first extended reality (XR) objects, wherein each first XR object is associated with one of the plurality of first objects and a plurality of second XR objects, wherein each second XR objects are associated with each of the plurality of second objects; 
 build a first XR display using the plurality of first XR objects and a second XR display using the plurality of second XR objects; and 
 indicate, via the XR platform, at least one discrepancy between the first XR display and the second XR display. 
 
     
     
       2. The system of  claim 1 , wherein the at least one processing device is further configured to:
 display, in response to a second user input, a suggested alternation associated with the at least one discrepancy. 
 
     
     
       3. The system of  claim 2 , wherein the processing device is further configured to determine the suggested alteration based on a second machine learning engine. 
     
     
       4. The system of  claim 1 , wherein the error is selected from a list of pending application errors. 
     
     
       5. The system of  claim 1 , wherein identifying a plurality of first XR objects and a plurality of second XR objects comprises querying a database. 
     
     
       6. The system of  claim 5 , wherein the database comprises a first set of metadata associated with a plurality of objects and a second set of metadata associated with a plurality of XR objects. 
     
     
       7. The system of  claim 2 , wherein the at least one processing device is further configured to automatically implement the suggested alteration to the source code associated with the error. 
     
     
       8. A computer program product for automated code resolution in an extended reality environment, the computer program product comprising a non-transitory computer-readable medium comprising code causing a first apparatus to:
 electronically receive a first user input comprising a selection of an error; 
 build a first display based on a plurality of application requirements associated with the error; 
 build a second display based on a source code associated with the error; 
 identify, via a first machine learning algorithm, a plurality of first objects associated with the first display and a plurality of second objects associated with the second display; 
 identify a plurality of first extended reality (XR) objects, wherein each first XR object is associated with one of the plurality of first objects and a plurality of second XR objects, wherein each second XR objects are associated with each of the plurality of second objects; 
 build a first XR display using the plurality of first XR objects and a second XR display using the plurality of second XR objects; and 
 indicate, via the XR platform, at least one discrepancy between the first XR display and the second XR display. 
 
     
     
       9. The computer program product of  claim 8 , wherein the first apparatus is further configured to:
 display, in response to a second user input, a suggested alternation associated with the at least one discrepancy. 
 
     
     
       10. The computer program product of  claim 9 , wherein the first apparatus is further configured to:
 determine the suggested alteration based on a second machine learning engine. 
 
     
     
       11. The computer program product of  claim 8 , wherein the error is selected from a list of pending application errors. 
     
     
       12. The computer program product of  claim 8 , wherein identifying a plurality of first XR objects and a plurality of second XR objects comprises querying a database. 
     
     
       13. The computer program product of  claim 12 , wherein the database comprises a first set of metadata associated with a plurality of objects and a second set of metadata associated with a plurality of XR objects. 
     
     
       14. The computer program product of  claim 9 , wherein the first apparatus is further configured to:
 automatically implement the suggested alteration to the source code associated with the error. 
 
     
     
       15. A method for automated code resolution in an extended reality environment, the method comprising:
 electronically receiving a first user input comprising a selection of an error; 
 building a first display based on a plurality of application requirements associated with the error; 
 building a second display based on a source code associated with the error; 
 identifying, via a first machine learning algorithm, a plurality of first objects associated with the first display and a plurality of second objects associated with the second display; 
 identifying a plurality of first extended reality (XR) objects, wherein each first XR object is associated with one of the plurality of first objects and a plurality of second XR objects, wherein each second XR objects are associated with each of the plurality of second objects; 
 building a first XR display using the plurality of first XR objects and a second XR display using the plurality of second XR objects; and 
 indicating, via the XR platform, at least one discrepancy between the first XR display and the second XR display. 
 
     
     
       16. The method of  claim 15 , further comprising:
 displaying, in response to a second user input, a suggested alternation associated with the at least one discrepancy. 
 
     
     
       17. The method of  claim 16 , further comprising:
 determining the suggested alteration based on a second machine learning engine. 
 
     
     
       18. The method of  claim 15 , wherein the error is selected from a list of pending application errors. 
     
     
       19. The method of  claim 15 , wherein identifying a plurality of first XR objects and a plurality of second XR objects comprises querying a database, wherein the database comprises a first set of metadata associated with a plurality of objects and a second set of metadata associated with a plurality of XR objects. 
     
     
       20. The method of  claim 16 , further comprising:
 automatically implementing the suggested alteration to the source code associated with the error.